Spatial hearing refers to the fact that when a sound is emanating from a discrete position, the acoustic signals arriving at a listeners ears not only travel on a direct path from the sound source to the ear-canal entrance, but they also arrive after reflecting and diffracting around the human anatomy causing acoustic artefacts. These artefacts, which are often different for left and right ears, give the listener cues to localize the sound. These features of sound transmission that are related to a listener can be encapsulated in a digital electronic data structure or dataset, referred to as a head-related transfer function (HRTF). A single HRTF is a pair of acoustic filters (one for each ear) which characterize the acoustic transmission from one position in a reflection-free environment to respective microphones placed in the ears of a listener at a given position or pose of the listener. An HRTF is used by a binaural simulation digital signal processing algorithm, to reproduce an audio recording as binaural sound, through driving a pair of headphones worn by a listener. The process uses the HRTF to create the illusion of a sound source somewhere in the environment. They encapsulate the fundamentals of spatial hearing.
Due to physiological differences between humans' ears, head and body, an HRTF is highly individualized. Binaural simulation using non-individualized HRTFs (for example, a listener auditioning a simulation using the HRTF dataset of another person) can cause audible problems in both the perceived position and quality (timbre) of the virtual sound.
There are a number of methods to achieve individualized HRTFs but these are often time-consuming or practically unfeasible when implemented in a consumer electronic device setting. When HRTF individualization is not possible, a generic HRTF is often used which aims to represent the ‘average’ HRTF. An HRTF dataset can be broken down into a set of underlying parameters such as inter-aural time difference (ITD), inter-aural level differences (ILD) and diffuse field HRTF (DF-HRTF). This information is useful in the individualization of an HRTF dataset. For example, an average HRTF could be created as a composite HRTF dataset that contains the ITDs from one person and the ILDs of another person. If enough of the features are personalized, the composite HRTF dataset should be indistinguishable from a measurement of their own HRTF dataset.